Algorithm Improves Navigation of Autonomous Driving Robots

Researchers have designed an algorithm that allows an autonomous ground vehicle to improve its existing navigation systems by watching a human drive. The approach — called adaptive planner parameter learning from demonstration (APPLD) — fused machine learning from demonstration algorithms and more classical autonomous navigation systems. Rather than completely replacing a classical system, APPLD learns how to tune the existing system to behave more like the human demonstration. The deployed system retains all the benefits of classical navigation systems — such as optimality, explainability, and safety — while also allowing the system to be flexible and adapt to new environments.